Medicare star quality management: Charting a path to higher ratings
The centers for medicare and medicaid services (CMS) is committed to achieving the 13 to 14 percent nationwide cuts outlined in the affordable care act (ACA), which will hit plans fully by 2017. In an industry where many plans operate with margins in the low single digits, any reduction is a daunting figure. One of the most promising strategies to offset this loss of revenue is to capture bonus payments from the five-star quality rating system, which can reduce the cuts from 14 percent to 9 percent if a health plan achieves at least a 4-star overall rating. Improving performance to capture these bonuses won’t be easy, but the rewards will be significant.
Medicare star quality management Charting a path to higher ratings
About the authors
Chicago Mike Connolly Senior Partner +1-312-578-4580 mike.connolly @strategyand.pwc.com Akshay Jindal Principal +1-312-578-4601 akshay.jindal @strategyand.pwc.com
New York Gil Irwin Senior Partner +1-212-551-6548 gil.irwin @strategyand.pwc.com Joyjit Saha Choudhury Partner +1-212-551-6871 joyjit.sahachoudhury @strategyand.pwc.com Sundar Subramanian Partner +1-212-551-6651 sundar.subramanian @strategyand.pwc.com
San Francisco Thom Bales Partner +1-415-627-3371 thom.bales @strategyand.pwc.com
Hector Rincon was formerly a senior associate with Strategy&. Joyjit Saha Choudhury is a partner with Strategy&’s global health practice based in New York. He has extensive experience serving payors and providers on topics of corporate and business unit strategy. He co-leads the firm’s medical value management practice and the firm’s Medicare and Medicaid Center of Excellence (MCoE). Sundar Subramanian is a partner with Strategy&’s global health and operations practices based in New York. He co-leads the firm’s Medicare and Medicaid Center of Excellence (MCoE), and the core operations offerings in health. He supports health plans, pharmacy benefit managers, and services companies in developing strategies, business models, and transformation efforts.
Minh Chau and Abhijeet Rawle also contributed to this report.
This report was originally published by Booz & Company in 2013.
The Call Letter issued April 1 by the Centers for Medicare & Medicaid Services (CMS) announced a 2 to 3 percent cut for Medicare Advantage (MA) plans in 2014. This is a valuable reminder that CMS is committed to achieving the 13 to 14 percent nationwide cuts outlined in the Affordable Care Act (ACA), which will hit plans fully by 2017. The impact on each plan is tied to the cost of the counties it currently serves; higher-cost counties will experience larger cuts. But regardless of the current footprint, in an industry where many plans operate with margins in the low single digits, any reduction is a daunting figure. One of the most promising strategies to offset this loss of revenue is to capture bonus payments from the Five-Star Quality Rating System, which can reduce the cuts from 14 percent to 9 percent if a health plan achieves at least a 4-star overall rating. Improving performance to capture these bonuses won’t be easy, but the rewards will be significant, in terms of both additional revenue and greater ability to sign up new customers and expand the business. For health plans to chart and execute a path to higher ratings, Strategy& proposes a Star Improvement Framework with three key solution recommendations: 1. Define a path toward targeted improvement efforts. It’s critical to devote resources only to those measures with the highest expected impact on overall ratings. Strategy& has designed the Star Optimizer tool to make data-driven, plan-specific recommendations among the more than 50 star measures. Once the measures are chosen, the company must design initiatives and then test its assumptions through scenario analysis before execution. 2. Implement organizational enablers to ensure sustainability. Star measures are highly cross-functional; thus, governance structures and incentives that break down organizational silos to boost collaboration across functions are critical to succeed.
3. Execute on foundational capabilities required for plan excellence in care delivery and member experience. To meet requirements, plans must develop foundational capabilities that span the value chain, with a focus on care management, network management, and member services. Plans should then enhance these capabilities to meet the specific goals of their chosen measures.
The case for star quality management
Medicare health plans are coming under intense revenue pressure as reductions in Medicare Advantage (MA) reimbursements mandated by the Affordable Care Act (ACA) kick in. In 2014, reimbursements will decline 2 to 3 percent on average. By 2017, cuts are scheduled to reach the law’s target of a 14 percent reduction from pre-ACA levels on a national basis. The degree of plan impact is tied to the cost of the counties a plan currently serves; higher-cost counties will experience larger cuts. But regardless of the current footprint, in an industry where margins are routinely in the low single digits, that kind of revenue loss could be devastating, and plans that do not find ways to offset these cuts may be forced to exit the market. One of the few levers in the law that give health plans an opportunity to reclaim some of this revenue is the Five-Star Quality Rating System, which encourages high-quality healthcare by linking a plan’s performance with bonus payments. Under the program, the Centers for Medicare & Medicaid Services (CMS) rates all Medicare plans on a scale of 1 to 5 stars, with 1 star representing poor performance and 5 stars representing excellent performance. The scores in 2014 will be based on 51 measures — everything from annual flu vaccines, to osteoporosis management, to timely appeal reviews. The data will be collected in three plan and beneficiary information surveys — the Healthcare Effectiveness Data and Information Set (HEDIS), the Consumer Assessment of Health Providers & Systems (CAHPS), and the Medicare Health Outcomes Survey (HOS) — as well as administrative data from CMS. High scores will be rewarded. Through a combination of county-specific benchmark bonuses and rebates (received when plans bid below county benchmarks), plans can potentially receive 5 percent in additional reimbursements if they improve from 3 to 4 stars. According to Strategy& analysis, this equates to about US$40 per member per month on average. These revenue enhancements are significant (see Exhibit 1, next page), and they are not the only benefits of a high rating. There are also marketing, branding, and expansion benefits. Under the law, 5-star
Through county-specific benchmark bonuses and rebates, plans can potentially receive 5 percent in additional reimbursements.
plans are allowed to market and enroll beneficiaries throughout the year, and beneficiaries can switch from their current plan to a 5-star plan at any time. Another benefit is that during open enrollment, 5-star plans get a “high performer” icon next to their names on the Medicare Plan Finder website, while plans with star ratings below 3 carry a “low performer” icon. Quality ratings also affect the CMS compliance performance scorecard — a key determinant of bid expansion decisions. In fact, plans with star ratings below 3 for three consecutive years are ineligible for new enrollment and are subject to termination. For all these reasons, plans that can manage their ratings adroitly will gain a significant advantage over competitors. The few plans that can simultaneously excel in the kind of efficient care and network management that result in true cost advantages will become even bigger winners in this market. A more holistic and deliberate approach for quality management with a focus on star criteria is demanded.
Exhibit 1 Projected decline in reimbursements and potential offset of quality bonus payments for high star performers
Dollars per member per month $1,000 Pre-ACA Reimbursement
Net Reimbursement (>=4 stars)
Note: Projections assume Congress will delay drastic cuts in Medicare physician payments. Net reimbursement includes impact of coding intensity changes and the health insurance tax. Star impact is based on typical incremental value of moving from 3 to 4 stars. Source: Citi Managed Care Analyst Report; Commonwealth Fund Analyst Reports; 2014 Final Call Letter; Strategy& analysis
Net Reimbursement (<4 Stars) $0 2010 2011 2012 2013 2014 2015 2016 2017
The Strategy& star improvement framework
Most plans face a long road to excellence in the star system. Based on 2013 quality ratings, only 23 percent of plans reached the necessary 4-star threshold to receive bonuses in 2015; meanwhile, nearly two-thirds of Medicare Advantage Prescription Drug (MAPD) beneficiaries are in plans with fewer than 4 stars. This underperformance is somewhat understandable. Star quality metrics are complex and pose many challenges. As noted, the star measures are derived from disparate sources, including HEDIS, HOS, CAHPS, and CMS administrative data. The measures also cut across all aspects of care, requiring coordination among multiple stakeholders (including providers, members, business unit leaders, and senior executives). Making matters more complicated, the star methodology can shift from year to year. For instance, CMS is seeking to raise the 4-star thresholds in 2015 for six metrics relating to the Million Hearts initiative (which aims to prevent 1 million heart attacks and strokes by 2017). Finally, there is a lag in the data’s impact on a star score, and that lag varies among the 51 measures. This means that in any given year the score is based on data collected 12 to 24 months in the past. Thus, health plans need to think very carefully about how they manage their star program and sequence improvement initiatives. In response to this complex and often confusing new environment, plans should resist making too many changes at once and focus foremost on those measures that they can most easily improve and that have the highest potential impact on their overall star rating. Along with prioritizing which measures to focus on and designing initiatives to impact those measures, the plan must build organizational enablers to ensure coordination and accountability across the company, as well as foundational capabilities (aligned to the measures it has prioritized) to ensure optimal performance across the front-, middle-, and backoffice functions. Adhering to the recommendations outlined by Strategy&’s Star Improvement Framework can provide a substantial boost to a plan’s
Exhibit 2 Strategy& star improvement framework
Deﬁne a path
Objectively prioritize measures and focus initiatives
Analytics and prioritization
Build organizational enablers to ensure coordination and execution across multiple stakeholders with clear accountability
Build for the long term
Develop foundational capabilities critical to general health plan performance and aligned to star strategy Front ofﬁce
Middle ofﬁce Back ofﬁce
Note: Front-office capabilities have relatively low impact on star ratings. Source: Strategy&
efforts (see Exhibit 2). For example, if the organization decides to focus on improving kidney disease monitoring for diabetics, it needs the organizational enablers to set and monitor goals and the foundational capabilities to identify at-risk members, provide educational materials about the condition to individual members, and educate providers on improvement goals. We discuss this in more detail later in this report. Analytics and prioritization There are three steps to defining the most effective approach to boosting a summary plan score: (1) prioritize measures based on their expected impact; (2) identify the initiatives you need to undertake to improve the measures you have prioritized; and (3) test the priorities and the supporting initiatives.
Prioritize measures based on their expected impact First, a plan should identify which of the 51 measures defined by CMS it will report on. (Not all plans report on all 51.) Next, determine which of those measures have the greatest potential for improvement and the highest likely impact on the overall star rating. To facilitate the prioritization process, we have developed the Strategy& Star Optimizer tool. The Star Optimizer tool uses public information from CMS and the company’s own performance data to make unbiased recommendations based on the likelihood that different measures can be improved and the subsequent impact. In our view, it’s critical to use an unbiased, datadriven approach to set priorities, given the cross-functional nature of the measures and the sometimes competing interests of stakeholders. To illustrate how the Star Optimizer tool determines the likelihood of improvement, consider a plan trying to decide whether to focus on improving “controlling blood pressure” or “breast cancer screening” from 3 to 4 stars. Our analysis found that 55 percent of health plans have a rating of 4 or 5 stars for “controlling blood pressure,” but only 30 percent are rated that high for “breast cancer screening” (see Exhibit 3, next page). It’s reasonable to assume that reaching 4 stars in the former is easier than in the latter. Working on metrics that are relatively easy to move based on historical, plan-specific data boosts the likelihood of success. The second determinant for “likelihood of improvement” is how close the health plan is to the next performance threshold. All things being equal, it’s easier for a health plan to improve its performance by 5 percent to reach 4 stars than by 10 percent. Thus, to improve their chance of success, plans should focus on those measures closest to the next threshold. In parallel, the Star Optimizer tool evaluates the impact of the individual measure’s improvement on the company’s overall summary score. The impact of a particular improvement is calculated in part by factoring in the measure’s weight assigned by CMS in the overall score, which varies from 1 to 3. For instance, “breast cancer screening” has a weight of 1 while “controlling blood pressure” has a weight of 3. However, other factors affect the overall impact. CMS rewards plans whose individual measures have a low deviation from their overall score. This is to guard against plans choosing to be very good at some aspects of care and very bad at others. Hence, a company should take into account both the weight and the individual measure’s deviation from the overall plan score when determining the impact of improvement.
Exhibit 3 Strategy& Star Optimizer tool: Prioritize measures based on expected impact — illustrative example
Impartial data-driven inputs
Current and target (4-star) performance Metric A B Breast cancer screening Controlling blood pressure
Summary rating = 3.52
Likelihood % plans above next threshold 30% Performance gap to next threshold 9.4% Measure weight 1
Impact Deviation from overall rating 0.52
Measure star rating 3
Current performance 64
Target performance 74
Actionable view of expected impact
Breast cancer screening Low potential “Smaller bang for the buck” • Lower percentage of plans above next threshold • Larger gap to next threshold • Lower metric weight • Similar deviation from plan’s overall star rating
Likelihood of improvement
High potential Controlling blood pressure B
Controlling blood pressure High potential “Bigger bang for the buck” • Higher percentage of plans above next threshold • Smaller gap to next threshold • Higher metric weight • Similar deviation from plan’s overall star rating
Low potential Breast A cancer screening
Impact on star rating
Bringing all these drivers together, a health plan can gain a clear view of which measures have the highest expected impact. Relative performance and the gap to the next threshold determine the likelihood of improvement, while the measure’s weight and its deviation are the key determinants of impact. The final view combines these elements and allows health plans to quickly map and identify those measures with the highest expected impact. In the example in Exhibit 3, “controlling blood pressure” has a higher potential impact than “breast cancer screening” because there are more plans at 4 stars or above, a lower gap to the next threshold, a higher metric weight, and a similar deviation to the plan’s current rating.
Identify initiatives to improve prioritized measures Once health plans prioritize measures, they need to identify the root causes of underperformance and identify solutions. For example, if a plan chooses to focus on the “controlling blood pressure” measure, it needs to identify the members at highest risk of blood pressure ailments and the providers most likely to serve them. Armed with this information, the health plan can design a highly targeted education campaign that encourages members to discuss the issue with their physicians and reminds physicians to ask members about the condition during office visits. The health plan might also choose to launch a proactive, telephone-based outreach campaign to contact high-risk members. When choosing among these initiatives, health plans need to consider the potential return on investment. This requires them to estimate the initiative’s contribution to improving a particular measure and the improved measure’s impact on the summary plan score. Health plans should also consider the initiative’s breadth of impact. For instance, an outreach campaign could target several high-impact measures at once. Confirming the path to improvement Once a list of targeted measures and improvement initiatives is finalized, health plans should test their assumptions by using techniques such as the Monte Carlo method to approximate the likelihood of different results by running multiple trial runs. These simulations give decision makers a range of possible outcomes and the probabilities that they will occur. It shows the extreme possibilities for very aggressive and very conservative decisions, as well as the consequences for middleof-the-road choices (see Exhibit 4, next page). This final step in defining a path is critical to validating the star improvement strategy. If the simulation goes as hoped, the company can move confidently from the planning stage to executing the initiatives. But if it uncovers a low probability of improvement, the company has the opportunity to step back and reevaluate the measures and initiatives it has targeted. Organizational enablers As noted, star ratings measure performance across a health plan’s entire spectrum of functions — care delivery, care access, member satisfaction, and customer service, among others. In fact, one measure specifically asks members to give the plan an “overall rating” that cuts across functional areas. Therefore, monitoring and driving
Health plans should test their assumptions by using simulations that give decision makers a range of possible outcomes and the probabilities that they will occur.
Exhibit 4 Modeling a path to success: Probability of improving overall star rating
Overall star rating distribution for following year
Probability of summary score
Probability of summary score
A Selecting the right path:
Focusing on a prioritized set of measures results in a higher likelihood of reaching 4 stars
B Selecting the wrong path:
Ad hoc selection of measures for investment increases variability and lowers likelihood of reaching 4 stars
improvement requires activities that break down organizational silos and encourage close collaboration among functional leaders. Health plans can address these issues by taking the following steps: • Establish C-level engagement. Given the cross-functional nature of measures, health plans need a senior executive to head the stars management steering committee and drive direction, accountability, and coordination. This involvement helps keep initiatives on track by, for instance, quickly resolving disputes among the managers who are directly responsible for improving individual measures. • Assign a dedicated team. The star program is critical and complex, and it requires the full attention of a dedicated team to manage the
necessary communication and processes. Furthermore, the team needs appropriate resources, including analytic tools to track performance, forecast enhancements, and monitor investments. • Align accountability and incentives. The leader with the greatest impact on a prioritized measure should have accountability and incentives for maintaining or improving performance. For instance, the bonuses of the head of appeals should be tied to outcomes for the “plans make timely decisions about appeals” measure. Leaders with measures that cut across functions should be empowered to financially incentivize managers who contribute toward improvements. Foundational capabilities Star quality management also requires a strong base of performanceoriented capabilities geared to meeting the performance requirements for care delivery and the Medicare beneficiary experience outlined by CMS. Most of these capabilities are concentrated in network management, medical management, and member services due to their direct impact on the delivery of care and the beneficiary experience. Network management capabilities include systems to support incentive programs (such as pay for performance) that align providers with star measure goals and provider coordination tools (such as wireless devices and portals that facilitate exchange of information across providers). Medical management capabilities include data, processes, and tools to identify high-risk members and develop coordinated care plans across providers. And member services capabilities include customer service portals that provide an integrated view of members — including complaint history and typical care needs — to ensure that their health concerns are handled and routed appropriately. Each plan needs to customize, to a certain degree, these organizational enablers and foundational capabilities to align with their own star improvement objectives. If, for example, a plan chose to address kidneydisease monitoring for diabetic members, it would need to appoint a manager to oversee kidney-disease monitoring and empower that person to enforce cross-functional cooperation across marketing (for educational programs), network management, and medical management. On the foundational side, the plan needs the analytic tools to assess the adequacy of the diabetes specialists in the network, and the data and teams to create programs that meet the specific needs of the at-risk population. In addition, it needs the more nuanced data and tools to identify high-risk diabetic members (see Exhibit 5, next page).
Exhibit 5 Organizational enablers and foundational capabilities aligned to star strategy — kidney-disease monitoring for diabetic members example
Steering committee prioritizes measures and establishes goals for kidney-disease monitoring measure Dedicated team actively monitors and tracks performance for target measures • Accountable owner has incentives directly aligned to performance of kidney-disease monitoring
Analytical team to monitor and ensure adequate provider network to support diabetic members • Incentives aligning targeted providers to star measure goal
Predictive modeling tools to identify and place diabetic members in disease management programs • Tracking tools to monitor execution of kidney function tests
Customer service desktop with single view of member leveraged to provide checkup reminders to diabetic members • Call tiering tools to route member concerns properly
As reductions in Medicare reimbursements begin to bite, health plans need to offset lost revenue. One of the most promising routes is to enhance the star improvement program to earn bonuses and rebates associated with reaching at least 4 stars. In fact, the benefits of strong star management go beyond these financial incentives; a high star rating also strengthens a company’s hand by creating differentiation with rivals in a highly competitive Medicare health plan environment. By prioritizing improvement measures using the Star Optimizer tool, and then putting the right organizational enablers and foundational capabilities in place, health plans can position themselves to compete more effectively in the quickly changing healthcare landscape. Those plans that don’t manage the star program effectively through such a systematic approach may be forced to exit the business.
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This report was originally published by Booz & Company in 2013.
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